Fitting structural models to electricity prices

We develop a structural model for electricity spot prices that incorporates renewable generation into the multi-fuel bid-stack framework. We show that renewables can be modeled by adjusting the demand curve, effectively reducing the market to one with only conventional fuels. Under standard assumptions of inelastic demand and market equilibrium, we derive a general pricing model highlighting two special cases: a single conventional fuel and a proportional multi-fuel setting. Using German market data, we evaluate structural models within this framework and compare their out-of-sample performance with standard benchmark models, including machine-learning and ARX-type specifications. Our findings indicate that a theory-driven one-fuel structural model, using gas prices, EUA prices, and residual demand as driving variables, delivers the most robust out-of-sample performance. These results highlight the practical value of theoretical structural models, particularly their simplicity and interpretability.

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Publication Details

Journal
Quantitative Finance
Published
2026-10-29
DOI
https://doi.org/10.1080/14697688.2026.2733386
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Fitting structural models to electricity prices

Andreas Basse-O’Connor, Jan Pedersen, Lota Copic, Tor Bonde
Quantitative Finance
Stochastic processes and financial applications
article

Fitting structural models to electricity prices

Andreas Basse-O’Connor, Jan Pedersen, Lota Copic, Tor Bonde
article en

Abstract

We develop a structural model for electricity spot prices that incorporates renewable generation into the multi-fuel bid-stack framework. We show that renewables can be modeled by adjusting the demand curve, effectively reducing the market to one with only conventional fuels. Under standard assumptions of inelastic demand and market equilibrium, we derive a general pricing model highlighting two special cases: a single conventional fuel and a proportional multi-fuel setting. Using German market data, we evaluate structural models within this framework and compare their out-of-sample performance with standard benchmark models, including machine-learning and ARX-type specifications. Our findings indicate that a theory-driven one-fuel structural model, using gas prices, EUA prices, and residual demand as driving variables, delivers the most robust out-of-sample performance. These results highlight the practical value of theoretical structural models, particularly their simplicity and interpretability.

Quantitative Finance
Aarhus University (DK)
Innovationsfonden
Openalex Percentile: Top 9%
Stochastic processes and financial applications
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